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Human-detection-and-tracking-on-Railway-tracks

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# Human-detection-and-tracking-on-Railway-tracks
# Introduction

The motivation behind the project is the large number of collisions that occur between trains and various obstacles which has resulted in reduced safety and high costs. Continuous research is being carried out by distinct researchers to ensure railway safety and to reduce accident rates. In this project, a novel method is proposed for identifying objects (obstacles like humans , stray animals etc.) on the railway tracks in front of a moving train using a thermal camera. This approach presents a way of detecting the railway tracks as well as a deep network based method to recognize obstacles on the track. A pre-trained network is used that provides the model understanding of real world objects and enables deep learning classifiers for obstacle identification. The validation data is acquired by an open source video which shows thermal imaging using night vision IR camera. In this work, Faster R-CNN is used that efficiently recognize obstacles on the railway tracks.

This method will be also cost-effective as it doesn’t require any significant change in the train’s infrastructure as well as will reduce the financial burden of railways in terms of accidental compensations.

# About the Code
The whole code was developed in MATLAB and can be executed on any of the platforms(such as MATLAB or Octave) that support the language .

Human-detection-and-tracking-on-Railway-tracks

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